"CrateDB for IoT and Machine Data"
"CrateDB for IoT and Machine Data" is a comprehensive guide designed for data architects, engineers, and technology leaders confronting the challenges of managing large-scale IoT and machine-generated data. The book opens with a deep dive into the unique characteristics of IoT data, covering the immense scale, heterogeneity, and intermittent nature inherent in modern connected environments. Readers are equipped with a robust understanding of end-to-end data flows—from devices and gateways at the edge to backend systems in the cloud—while exploring the current landscape of time-series platforms and architectural design principles for scalable, secure solutions.
The text methodically explores CrateDB's distributed SQL architecture, uncovering the technical underpinnings of its columnar storage, sharding, partitioning, and high-availability mechanisms. Readers gain valuable insights into schema modeling tailored for diverse sensor data, efficient ingestion pipelines—streaming, ETL, and real-time processing—and strategies for indexing, validation, and anomaly detection. Advanced chapters delve into query optimization, data analytics, machine learning integration, security and compliance, and full-lifecycle DevOps automation, making the book a technical reference for both daily operations and innovation projects.
Through practical best practices and detailed case studies, the book illustrates blueprints for deploying CrateDB in demanding industrial IoT environments, tuning for performance, reliability, and compliance while integrating with the wider data ecosystem—BI tools, data lakes, edge computing, and cloud-native services. Whether you're modernizing an existing platform or architecting the next generation of IoT analytics, "CrateDB for IoT and Machine Data" combines foundational knowledge with actionable guidance, preparing professionals to unlock the full value of machine data at scale.